D. P. Van Vuuren, D. P. Van Vuuren, B. C. O'Neill
et al.
<p>Scenarios serve as a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations in this century. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the Earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study long-term impacts, climate change-related processes on long timescales, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of emissions and land use pathways for use by Earth system models and additional variants for adaptation and mitigation studies.</p>
André de Gouvêa, Hitoshi Murayama, Mark Palmer
et al.
In this document we summarize the output of the US community planning exercises for particle physics that were performed between 2020 and 2023 and comment upon progress made since then towards our common scientific goals. This document leans heavily on the formal report of the Particle Physics Project Prioritization Panel and other recent US planning documents, often quoting them verbatim to retain the community consensus.
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network's loss function, our approach simultaneously learns the quantum state's evolution and infers the underlying dissipation parameters from sparse, time-series measurement data. Our results show that PINNs can reconstruct both the system dynamics and the functional form of unknown noise parameters, presenting a sample-efficient and scalable solution for quantum device characterization. Ultimately, our method produces a fully-differentiable digital twin of a noisy quantum system by learning its governing master equation.
Inverse problems in the physical sciences are often ill-conditioned in input space, making progress step-size sensitive. We propose the Deceptron, a lightweight bidirectional module that learns a local inverse of a differentiable forward surrogate. Training combines a supervised fit, forward-reverse consistency, a lightweight spectral penalty, a soft bias tie, and a Jacobian Composition Penalty (JCP) that encourages $J_g(f(x))\,J_f(x)\!\approx\!I$ via JVP/VJP probes. At solve time, D-IPG (Deceptron Inverse-Preconditioned Gradient) takes a descent step in output space, pulls it back through $g$, and projects under the same backtracking and stopping rules as baselines. On Heat-1D initial-condition recovery and a Damped Oscillator inverse problem, D-IPG reaches a fixed normalized tolerance with $\sim$20$\times$ fewer iterations on Heat and $\sim$2-3$\times$ fewer on Oscillator than projected gradient, competitive in iterations and cost with Gauss-Newton. Diagnostics show JCP reduces a measured composition error and tracks iteration gains. We also preview a single-scale 2D instantiation, DeceptronNet (v0), that learns few-step corrections under a strict fairness protocol and exhibits notably fast convergence.
Kawawa Banda, Rasmus Jakobsen, Imasiku Nyambe
et al.
Study area: Machile-Zambezi Catchment. Study focus: Saline groundwater is a primary source of water insecurity in arid to semi-arid low recharge environments thus threatens the attainment of the sustainable development goals. In this paper, we investigate the potential of groundwater freshening in the Machile-Zambian Basin, hosting saline groundwater. Various methods were used that include hydro-geochemistry, environmental isotopes (δD/δO18, 3H/3He and C-13/C-14),and groundwater modelling (with PMWIN). New hydrological insights for the region: Fresh groundwater and brackish groundwater had a Ca-Mg-HCO3 /Na-HCO3 composition, whereas saline groundwater had a Na-Cl-SO4 composition. Stable isotopes indicate high evaporation in the saline zone propagating mineralisation under endorheic conditions driven likely by tectonics. The brackish and saline groundwater types had apparent ages of < 10 ka attributed to recharge during pluvial climatic events in the Holocene. Modflow modelling using PMWIN modelling showed that due to the hydraulic conductivity contrast in the host lithologies, fresh water in the basin is forced out through the topographic depressions and consumed by evapotranspiration with little or no interaction with the saline zone except in the transition zone. Aquifer structure, low recharge and high storage limits potential freshening of the saline region. Water security therefore requires investments in deep borehole drilling with an option to explore superficially near paleo-drainage channels.
Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the Aerosol domain-Adaptive Network (AAdaN). The framework utilizes a neural network to estimate mutual information, and aligns spatial covariate shift via a transfer loss term. Then, we assess the retrieval potential in unknown scenarios using independent land cover type, and the proposed model demonstrates satisfactory results. The cross-validation shows strong agreement with in-situ measurements, both in sample-based and site-based evaluations. Specifically, the site-based ten-fold cross-validation of our AOD retrievals indicate that all accuracy metrics are satisfactory, with a Pearson correlation of 0.766 and a Root-Mean-Square Error of 0.118, and that about 76.05 % of the retrievals meet the expected error criteria [±(0.05 + 20 %)]. Additionally, the proposed AAdaN achieves stable, high-accuracy aerosol retrievals across various surface and atmospheric conditions, and can generate spatially continuous AOD distributions. This study significantly improves spatial generalization and offers valuable insights for future model development.
Study region: The Heihe River Basin, China. Study focus: Irrigation data are often from census surveys at coarse administrative or river basin scale, and as such, the amount of water used for agricultural irrigation difficult to quantify. We improve the Soil Moisture to Rain (SM2RAIN) method to estimate irrigation water use in the Heihe River Basin from 2003 to 2020 using thermal infrared and microwave satellite data. The results showed that this approach has satisfactory performance in estimating the annual irrigation water volume (mean volume=0.657 km3/year, R2=0.83, RMSE=0.03 km3/year) when compared with the field measurements at irrigation district administrative scale, due to its reliability in determining the infiltrated water around the root zone used by crops. New hydrological insights for the region: Through an analysis of irrigation water use trends, the results indicate that most farmland areas exhibited a declining trend in water use per hectare (-55 m³/ha/yr). Interestingly, we observed that while water use efficiency improved significantly at the field scale, overall irrigation efficiency showed a decreasing trend. This study reveals a paradox in the Heihe River Basin, where enhanced irrigation efficiency rarely translates into reduced total water consumption at river basin scale. Our study advances agricultural irrigation volume estimation and irrigation mapping across district and river basin scales in arid and semi-arid areas, which should assist in irrigation scheduling and water resource management.
Léo Marconato, Marie-Pierre Doin, Laurence Audin
et al.
Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.
Merten Nikolay Dahlkemper, Simon Zacharias Lahme, Pascal Klein
This study aimed at evaluating how students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics comprehension questions. A total of 102 first- and second-year physics students were confronted with three questions of progressing difficulty from introductory mechanics (rolling motion, waves, and fluid dynamics). Each question was presented with four different responses. All responses were attributed to ChatGPT, but in reality one sample solution was created by the researchers. All ChatGPT responses obtained in this study were wrong, imprecise, incomplete, or misleading. We found little differences in the perceived linguistic quality between ChatGPT responses and the sample solution. However, the students rated the overall scientific accuracy of the responses significantly differently, with the sample solution being rated best for the questions of low and medium difficulty. The discrepancy between the sample solution and the ChatGPT responses increased with the level of self-assessed knowledge of the question content. For the question of highest difficulty (fluid dynamics) that was unknown to most students, a ChatGPT response was rated just as good as the sample solution. Thus, this study provides data on the students' perception of ChatGPT responses and the factors influencing their perception. The results highlight the need for careful evaluation of ChatGPT responses both by instructors and students, particularly regarding scientific accuracy. Therefore, future research could explore the potential of similar "spot the bot"-activities in physics education to foster students' critical thinking skills.
Terence J. O'Kane, Adam A. Scaife, Adam A. Scaife
et al.
Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy.
Gabriel A Cárdenas Belleza, Marc F P Bierkens, Michelle T H van Vliet
Water use for various sectors (e.g. irrigation, livestock, domestic, energy and manufacturing) is increasing due to a growing global population and economic development. Additionally, increases in frequency and severity of droughts, heatwaves and compound drought-heatwave events, also lead to responses in sectoral water use and a reduction in water availability, intensifying water scarcity. However, limited knowledge exists on the responses in sectoral water use during these hydroclimatic extremes. In this study we quantify the impacts of droughts, heatwaves and compound events on water use of irrigation, livestock, domestic, energy and manufacturing sectors at global, country and local scales. To achieve this, datasets of reported and downscaled sectoral water use (i.e. withdrawal and consumption) were evaluated during these hydroclimatic extremes and compared to normal (non-extreme) periods for 1990–2019. Our analysis shows that these hydroclimatic extremes affect water use patterns differently per sector and region. Reported data show that domestic and irrigation water use increases during heatwaves in Eastern Europe and central continental United States, while water use decreases for thermoelectric sector, particularly in Europe while it increases in north and Eastern Asia. Additionally, global water use response patterns reveal that irrigation and domestic sectors are mostly prioritized over livestock, thermoelectric and manufacturing. Reported local-scale data reveal that for most sectors and regions/locations, stronger water use responses are found for heatwaves and compound events compared to impacts during hydrological droughts. Our outcomes provide improved understanding of sectoral water use behaviour under hydroclimatic extremes. Nonetheless, given the future threats to water availability and the limited accessible information of water use, there is an urgency to collect more monitored-driven data of sectoral water use for improved assessments of water scarcity under these extremes. Consequently, this research reveals the necessity of more realistic water use models to better represent the sectoral responses to hydroclimatic extremes.
This article describes reflections on the Fifth International Conference on Women in Physics which was a conference attended by 215 female physicists and a few male physicists from 49 different countries. The article focuses on the barriers that women face in their professional advancement in physics and the extent to which the situation is different in various countries.
The proposed electron-proton collider experiments LHeC and FCC-eh at CERN are the highest resolution microscopes that can be realised in the present century and they would represent a really unique research facility. We exploit simulated neutral-current and charged-current deep-inelastic scattering data of the LHeC and the FCC-eh and examine their sensitivity for precision physics in the Electroweak sector of the Standard Model (SM), like the effective weak mixing angle $\sin^2θ_{\textrm{W},\ell}^\textrm{eff}$, or the light-quark weak-neutral-current couplings. Unique measurements are further feasible at high precision for the running of the weak mixing angle, as well as for electroweak effects in charged current interactions. The sensitivity to beyond SM effects is studied using the generic $S$, $T$ and $U$ parameterization. The report summarizes previous studies about the LHeC and presents new prospects for the FCC-eh.
<p>A rockfall dataset for Germany is analysed with the objective of identifying the meteorological and hydrological (pre-)conditions that change the probability for such events in central Europe. The factors investigated in the analysis are precipitation amount and intensity, freeze–thaw cycles, and subsurface moisture. As there is no suitable observational dataset for all relevant subsurface moisture types (e.g. water in rock pores and cleft water) available, simulated soil moisture and a proxy for pore water are tested as substitutes. The potential triggering factors were analysed both for the day of the event and for the days leading up to it.</p>
<p>A logistic regression model was built, which considers individual potential triggering factors and their interactions. It is found that the most important factor influencing rockfall probability in the research area is the precipitation amount at the day of the event, but the water content of the ground on that day and freeze–thaw cycles in the days prior to the event also influence the hazard probability. Comparing simulated soil moisture and the pore-water proxy as predictors for rockfall reveals that the proxy, calculated as accumulated precipitation minus potential evaporation, performs slightly better in the statistical model.</p>
<p>Using the statistical model, the effects of meteorological conditions on rockfall probability in German low mountain ranges can be quantified. The model suggests that precipitation is most efficient when the pore-water content of the ground is high. An increase in daily precipitation from its local 50th percentile to its 90th percentile approximately doubles the probability for a rockfall event under median pore-water conditions. When the pore-water proxy is at its 95th percentile, the same increase in precipitation leads to a 4-fold increase in rockfall probability. The occurrence of a freeze–thaw cycle in the preceding days increases the rockfall hazard by about 50 %. The most critical combination can therefore be expected in winter and at the beginning of spring after a freeze–thaw transition, which is followed by a day with high precipitation amounts and takes place in a region preconditioned by a high level of subsurface moisture.</p>
Intense anthropogenic activities in arid areas have great impacts on groundwater process by causing river dried-up and phreatic decline. Groundwater recharge and discharge have become hot spot in the dried-up river oases of arid regions, but are not well known, challenging water and ecological security. This study applied a stable isotope and end-member mixing analysis method to quantify shallow groundwater sources and interpret groundwater processes using data from 186 water samples in the Wei-Ku Oasis of central Asia. Results showed that shallow groundwater (well depth < 20 m) was mainly supplied by surface water and lateral groundwater flow from upstream, accounting for 88 and 12%, respectively, implying surface water was the dominant source. Stable isotopes and TDS showed obviously spatiotemporal dynamic. Shallow groundwater TDS increased from northwest to southeast, while the spatial variation trend of groundwater δ18O was not obvious. Surface water and groundwater in non-flood season had higher values of stable isotopes and TDS than those in flood season. Anthropogenic activities greatly affect groundwater dynamics, where land-cover change and groundwater overexploitation are the main driving factors. The findings would be useful for further understanding groundwater sources and cycling, and help restore groundwater level and desert ecosystem in the arid region. HIGHLIGHTS
The sources of shallow groundwater in the dried-up river oasis of central Asia were quantified.;
Surface water was the dominant source of shallow groundwater.;
Anthropogenic activities greatly affect groundwater dynamic and cycle.;
River, lake, and water-supply engineering (General), Physical geography
The ability to label and track physical objects that are assets in digital representations of the world is foundational to many complex systems. Simple, yet powerful methods such as bar- and QR-codes have been highly successful, e.g. in the retail space, but the lack of security, limited information content and impossibility of seamless integration with the environment have prevented a large-scale linking of physical objects to their digital twins. This paper proposes to link digital assets created through BIM with their physical counterparts using fiducial markers with patterns defined by Cholesteric Spherical Reflectors (CSRs), selective retroreflectors produced using liquid crystal self-assembly. The markers leverage the ability of CSRs to encode information that is easily detected and read with computer vision while remaining practically invisible to the human eye. We analyze the potential of a CSR-based infrastructure from the perspective of BIM, critically reviewing the outstanding challenges in applying this new class of functional materials, and we discuss extended opportunities arising in assisting autonomous mobile robots to reliably navigate human-populated environments, as well as in augmented reality.
Maximilian Graf, Abbas El Hachem, Micha Eisele
et al.
Study region: The study region is Germany and two sub-regions in Germany, i.e. the state of Rhineland-Palatinate and the city of Reutlingen.Study focus: Opportunistic rainfall sensors, namely personal weather stations and commercial microwave links, together with rain gauge data from the German Weather Service, were used in different combinations to derive rainfall maps with a geostatistical interpolation framework for Germany. This kriging type framework considered the uncertainty of opportunistic sensors and the line structure of commercial microwave links. The resulting rainfall maps were compared to two gauge-adjusted radar products and evaluated to three reference gauge datasets in the respective study regions on both daily and hourly basis.New Hydrological Insights for the Region: The interpolated rainfall products from opportunistic sensors provided good agreement to the reference rain gauges. The dataset combinations including information from the opportunistic sensors performed best. The addition of rain gauges from the German Weather Service did not consistently lead to an improvement of the interpolated rainfall maps. On the country-wide, daily scale the interpolated rainfall maps performed well, but the gauge-adjusted radar products were closer to the reference. For the regional and local scale in Rhineland-Palatinate and Reutlingen with an hourly resolution, the interpolated rainfall maps outperformed the interpolated product from DWD rain gauges and showed a similar agreement to the reference as the radar products.